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<li class="toctree-l1 current"><a class="current reference internal" href="#">6. Results on different datasets</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#id1">6.3.1. Dataset Statistics</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id2">6.3.2. Experiment Results</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#id3">6.4.1. Dataset Statistics</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id4">6.4.2. Experiment Results</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#id5">6.5.1. Dataset Statistics</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id6">6.5.2. Experiment Results</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="#id7">6.6.1. Dataset Statistics</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id8">6.6.2. Experiment Results</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#id9">6.7.2. Results on Bike</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#id10">6.7.2.1. Dataset Statistics</a></li>
<li class="toctree-l4"><a class="reference internal" href="#experiment-setting">6.7.2.2. Experiment Setting</a></li>
<li class="toctree-l4"><a class="reference internal" href="#id11">6.7.2.3. Experiment Results</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#id13">6.7.3.1. Dataset Statistics</a></li>
<li class="toctree-l4"><a class="reference internal" href="#id14">6.7.3.2. Experiment Setting</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#id20">6.7.5.2. Experiment Setting</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#id23">6.7.6.2. Experiment Setting</a></li>
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  <div class="section" id="results-on-different-datasets">
<h1>6. Results on different datasets<a class="headerlink" href="#results-on-different-datasets" title="Permalink to this headline">¶</a></h1>
<div class="section" id="stmeta-version">
<h2>6.1. STMeta Version<a class="headerlink" href="#stmeta-version" title="Permalink to this headline">¶</a></h2>
<p>As introduced in <a class="reference external" href="./static/current_supported_models.html#stmeta">Currently Supported Models</a>, STMeta is a meta-model that can be implemented by different deep learning techniques based on its applications. Here we realize three versions of STMeta to evaluate its generalizability. The main differences between these three variants are the techniques used in spatio-temporal modeling and aggregation units:</p>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Version Name</th>
<th align="center">Spatio-Temporal Unit</th>
<th align="center">Temporal Aggregation Unit</th>
<th align="center">Spatial Aggregation Unit</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">STMeta-V1</td>
<td align="center"><a href="../UCTB.model_unit.html?highlight=gclstmcel#UCTB.model_unit.ST_RNN.GCLSTMCell">GCLSTM</a></td>
<td align="center"><a href="../UCTB.model_unit.html?highlight=gclstmcel#UCTB.model_unit.GraphModelLayers.GAL">GAL</a></td>
<td align="center">GAL</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">GCLSTM</td>
<td align="center">Concat &amp; Dense</td>
<td align="center">GAL</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center"><a href="../UCTB.model_unit.html?highlight=gclstmcel#UCTB.model_unit.DCRNN_CELL.DCGRUCell">DCGRU</a></td>
<td align="center">GAL</td>
<td align="center">GAL</td>
</tr>
</tbody>
</table><p>By default, we use <code class="docutils literal notranslate"><span class="pre">STMeta-V1</span></code> to run LSTM and single graph model tests.</p>
<p>References:</p>
<ul class="simple">
<li><p>GCLSTM (Graph Convolutional Long short-term Memory):
<a class="reference external" href="https://arxiv.org/pdf/1807.10934">Chai, D., Wang, L., &amp; Yang, Q. (2018, November). Bike flow prediction with multi-graph convolutional networks</a></p></li>
<li><p>DCGRU (Diffusion Convolutional Gated Recurrent Unit):
<a class="reference external" href="https://arxiv.org/pdf/1707.01926.pdf">Li, Y., Yu, R., Shahabi, C., &amp; Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting</a></p></li>
<li><p>GAL (Graph Attention Layer):
<a class="reference external" href="https://arxiv.org/pdf/1710.10903.pdf">Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., &amp; Bengio, Y. (2017). Graph attention networks</a></p></li>
</ul>
<p>We conducted experiments on the following datasets at the granularity of 15 minutes, 30 minutes and 60 minutes respectively. Our running code and detailed parameter settings can be found in <a class="reference external" href="./all_results.html#experiment-setting-on-different-datasets">Experiment Setting</a>.</p>
</div>
<div class="section" id="results-on-bike">
<h2>6.2. Results on Bike<a class="headerlink" href="#results-on-bike" title="Permalink to this headline">¶</a></h2>
<div class="section" id="dataset-statistics">
<h3>6.2.1. Dataset Statistics<a class="headerlink" href="#dataset-statistics" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>New York City</strong></th>
<th align="center"><strong>Chicago</strong></th>
<th align="center"><strong>DC</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2013.03-2017.09</td>
<td align="center">2013.07-2017.09</td>
<td align="center">2013.07-2017.09</td>
</tr>
<tr>
<td align="center">Number of riding records</td>
<td align="center">49,100,694</td>
<td align="center">13,130,969</td>
<td align="center">13,763,675</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">820</td>
<td align="center">585</td>
<td align="center">532</td>
</tr>
</tbody>
</table><p>Following shows the map-visualization of bike stations in NYC, Chicago and DC.</p>
<p><img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/Bike_NYC.jpg" style="zoom:30%;height:800px;width:800px;" /> <img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/Bike_Chicago.jpg" style="zoom:30%;height:800px;width:800px;"/> <img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/Bike_DC.jpg" style="zoom:30%;height:800px;width:800px;" /></p>
</div>
<div class="section" id="experiment-results">
<h3>6.2.2. Experiment Results<a class="headerlink" href="#experiment-results" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center"><strong>15 minutes</strong></th>
<th align="center">NYC</th>
<th align="center">Chicago</th>
<th align="center">DC</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">1.89180</td>
<td align="center">1.66782</td>
<td align="center">1.55471</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">1.87415</td>
<td align="center">1.78399</td>
<td align="center">1.68858</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">1.71216</td>
<td align="center">1.67219</td>
<td align="center">1.55872</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">1.70757</td>
<td align="center">1.66691</td>
<td align="center">1.55246</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">1.68659</td>
<td align="center">1.64642</td>
<td align="center">1.54455</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">1.71223</td>
<td align="center">1.71789</td>
<td align="center">1.59412</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">1.98866</td>
<td align="center">1.80222</td>
<td align="center">1.67762</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">1.81819</td>
<td align="center">1.62269</td>
<td align="center">1.54041</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">1.65939</td>
<td align="center"><strong>1.60743</strong></td>
<td align="center">1.52698</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">1.67336</td>
<td align="center">1.62883</td>
<td align="center"><strong>1.51158</strong></td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center"><strong>1.65351</strong></td>
<td align="center">1.60917</td>
<td align="center">1.51720</td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center"><strong>30 minutes</strong></th>
<th align="center">NYC</th>
<th align="center">Chicago</th>
<th align="center">DC</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">2.68564</td>
<td align="center">2.22987</td>
<td align="center">1.95601</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">3.17849</td>
<td align="center">2.42798</td>
<td align="center">2.22804</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">2.70377</td>
<td align="center">2.37553</td>
<td align="center">1.95560</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">2.68164</td>
<td align="center">2.35532</td>
<td align="center">1.92799</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">2.51288</td>
<td align="center">2.17659</td>
<td align="center">1.90305</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">2.61848</td>
<td align="center">2.24642</td>
<td align="center">2.11771</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">3.01836</td>
<td align="center">2.49270</td>
<td align="center">2.21191</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">2.51124</td>
<td align="center">2.13333</td>
<td align="center">1.92748</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center"><strong>2.40976</strong></td>
<td align="center">2.17032</td>
<td align="center">1.85628</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">2.41088</td>
<td align="center"><strong>2.13330</strong></td>
<td align="center">1.85876</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">2.41109</td>
<td align="center">2.18174</td>
<td align="center"><strong>1.85199</strong></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center"><strong>60 minutes</strong></th>
<th align="center">NYC</th>
<th align="center">Chicago</th>
<th align="center">DC</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">3.992</td>
<td align="center">2.976</td>
<td align="center">2.631</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">5.609</td>
<td align="center">3.835</td>
<td align="center">3.604</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">4.124</td>
<td align="center">2.925</td>
<td align="center">2.656</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">3.999</td>
<td align="center">2.842</td>
<td align="center">2.617</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">3.723</td>
<td align="center">2.883</td>
<td align="center">2.485</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">4.186</td>
<td align="center">3.277</td>
<td align="center">3.086</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">4.556</td>
<td align="center">3.370</td>
<td align="center">2.915</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">3.784</td>
<td align="center">2.790</td>
<td align="center">2.547</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">3.504</td>
<td align="center"><strong>2.655</strong></td>
<td align="center">2.425</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center"><strong>3.438</strong></td>
<td align="center">2.663</td>
<td align="center">2.411</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">3.478</td>
<td align="center">2.661</td>
<td align="center"><strong>2.388</strong></td>
</tr>
</tbody>
</table></div>
</div>
<div class="section" id="results-on-didi">
<h2>6.3. Results on DiDi<a class="headerlink" href="#results-on-didi" title="Permalink to this headline">¶</a></h2>
<div class="section" id="id1">
<h3>6.3.1. Dataset Statistics<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>Xi'an</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2016.10-2016.11</td>
<td align="center">2016.10-2016.11</td>
</tr>
<tr>
<td align="center">Number of records</td>
<td align="center">5,922,961</td>
<td align="center">8,439,537</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">256</td>
<td align="center">256</td>
</tr>
</tbody>
</table><p>Following shows the map-visualization of grid-based ride-sharing stations in Xi’an and Chengdu.</p>
<p><img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/DiDi_Xian.jpg" style="zoom:30%;height:800px;width:800px;" /> <img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/DiDi_Chengdu.jpg" style="zoom:30%;height:800px;width:800px;" /></p>
</div>
<div class="section" id="id2">
<h3>6.3.2. Experiment Results<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">15 minutes</th>
<th align="center"><strong>Xian</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">2.82784</td>
<td align="center">3.34701</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">3.08823</td>
<td align="center">3.94791</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">2.79919</td>
<td align="center">3.42954</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">2.77484</td>
<td align="center">3.36292</td>
</tr>
<tr>
<td align="center">ST-ResNet</td>
<td align="center">2.68641</td>
<td align="center">3.31394</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">2.71417</td>
<td align="center">3.29300</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">2.88934</td>
<td align="center">3.74328</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">3.05138</td>
<td align="center">3.88830</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">2.91712</td>
<td align="center">3.28553</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">2.65320</td>
<td align="center">3.24408</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center"><strong>2.63673</strong></td>
<td align="center"><strong>3.24125</strong></td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">2.64849</td>
<td align="center">3.25353</td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">30 minutes</th>
<th align="center"><strong>Xian</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">4.23940</td>
<td align="center">4.85059</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">5.03538</td>
<td align="center">6.61832</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">4.17160</td>
<td align="center">4.91460</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">4.13541</td>
<td align="center">4.87335</td>
</tr>
<tr>
<td align="center">ST-ResNet</td>
<td align="center">3.90263</td>
<td align="center">4.67278</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">3.88621</td>
<td align="center">4.73162</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">4.52889</td>
<td align="center">6.25836</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">4.95037</td>
<td align="center">6.44397</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">3.84690</td>
<td align="center">4.67784</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">3.80812</td>
<td align="center">4.64988</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center"><strong>3.77190</strong></td>
<td align="center"><strong>4.61277</strong></td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">3.83278</td>
<td align="center">4.63502</td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center"><strong>Xian</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">6.186</td>
<td align="center">7.354</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">9.474</td>
<td align="center">12.526</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">6.733</td>
<td align="center">7.738</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">6.446</td>
<td align="center">7.588</td>
</tr>
<tr>
<td align="center">ST-ResNet</td>
<td align="center">6.084</td>
<td align="center">7.146</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">5.874</td>
<td align="center"><strong>7.032</strong></td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">8.202</td>
<td align="center">11.505</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">7.399</td>
<td align="center">10.113</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">5.814</td>
<td align="center">7.048</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">5.891</td>
<td align="center">7.062</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center"><strong>5.755</strong></td>
<td align="center">7.097</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">5.955</td>
<td align="center">7.043</td>
</tr>
</tbody>
</table></div>
</div>
<div class="section" id="results-on-metro">
<h2>6.4. Results on Metro<a class="headerlink" href="#results-on-metro" title="Permalink to this headline">¶</a></h2>
<div class="section" id="id3">
<h3>6.4.1. Dataset Statistics<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>Chongqing</strong></th>
<th align="center"><strong>Shanghai</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2016.08-2017.07</td>
<td align="center">2016.07-2016.09</td>
</tr>
<tr>
<td align="center">Number of records</td>
<td align="center">409,277,117</td>
<td align="center">333,149,034</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">113</td>
<td align="center">288</td>
</tr>
</tbody>
</table><p>Following shows the map-visualization of metro stations in Chongqing and Shanghai.</p>
<p><img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/Metro_Chongqing.jpg" style="zoom:30%;height:800px;width:800px;" /> <img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/Metro_Shanghai.jpg" style="zoom:30%;height:800px;width:800px;" /></p>
</div>
<div class="section" id="id4">
<h3>6.4.2. Experiment Results<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">15 minutes</th>
<th align="center"><strong>Chongqing</strong></th>
<th align="center"><strong>Shanghai</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">45.25524</td>
<td align="center">49.74561</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">67.11072</td>
<td align="center">83.53750</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">35.69683</td>
<td align="center">47.88690</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">33.28726</td>
<td align="center">44.55068</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">32.71874</td>
<td align="center">46.54292</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">37.06903</td>
<td align="center">56.00411</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">55.36633</td>
<td align="center">80.40264</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">33.34361</td>
<td align="center">45.88331</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center"><strong>31.39239</strong></td>
<td align="center">41.66834</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">38.20912</td>
<td align="center">43.82808</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">36.90250</td>
<td align="center"><strong>40.94003</strong></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center"><strong>30 minutes</strong></th>
<th align="center">Chongqing</th>
<th align="center">Shanghai</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">74.54662</td>
<td align="center">108.59372</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">180.53262</td>
<td align="center">212.00777</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">69.50227</td>
<td align="center">81.82434</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">72.98518</td>
<td align="center">83.93989</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">50.95764</td>
<td align="center">88.76412</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">65.71969</td>
<td align="center">116.14510</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">104.60832</td>
<td align="center">195.60097</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">53.17723</td>
<td align="center">85.19422</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">49.46800</td>
<td align="center"><strong>75.36282</strong></td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">50.01080</td>
<td align="center">80.68939</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center"><strong>48.95798</strong></td>
<td align="center">77.48744</td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center"><strong>Chongqing</strong></th>
<th align="center"><strong>Shanghai</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">120.30</td>
<td align="center">197.97</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">578.18</td>
<td align="center">792.15</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">117.05</td>
<td align="center">185.00</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">113.92</td>
<td align="center">186.74</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">118.86</td>
<td align="center">181.55</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">122.31</td>
<td align="center">326.97</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">196.17</td>
<td align="center">368.84</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">97.50</td>
<td align="center">182.28</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center"><strong>92.74</strong></td>
<td align="center"><strong>151.11</strong></td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">98.86</td>
<td align="center">158.21</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">101.78</td>
<td align="center">156.58</td>
</tr>
</tbody>
</table></div>
</div>
<div class="section" id="results-on-charge-station">
<h2>6.5. Results on Charge-Station<a class="headerlink" href="#results-on-charge-station" title="Permalink to this headline">¶</a></h2>
<div class="section" id="id5">
<h3>6.5.1. Dataset Statistics<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>Beijing</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2018.03-2018.05</td>
</tr>
<tr>
<td align="center">Number of records</td>
<td align="center">1,272,961</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">629</td>
</tr>
</tbody>
</table><p>Following shows the map-visualization of  629 EV charging stations in Beijing.</p>
<img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/EV_Beijing.jpg" style="zoom:40%;height:800px;width:800px;" /></div>
<div class="section" id="id6">
<h3>6.5.2. Experiment Results<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">30 minutes</th>
<th align="center"><strong>Beijing</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">0.86361</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">0.75522</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">0.68649</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">0.68931</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">0.69083</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">0.75740</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">0.75474</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">0.68627</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">0.66985</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center"><strong>0.66675</strong></td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">0.66966</td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center"><strong>Beijing</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">1.016</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">0.982</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">0.833</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">0.828</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">0.827</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">0.988</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">1.585</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">0.833</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center"><strong>0.815</strong></td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">0.821</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center"><strong>0.815</strong></td>
</tr>
</tbody>
</table></div>
</div>
<div class="section" id="results-on-traffic-speed">
<h2>6.6. Results on Traffic Speed<a class="headerlink" href="#results-on-traffic-speed" title="Permalink to this headline">¶</a></h2>
<div class="section" id="id7">
<h3>6.6.1. Dataset Statistics<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2012.03-2012.06</td>
<td align="center">2017.01-2017.07</td>
</tr>
<tr>
<td align="center">Number of riding records</td>
<td align="center">34,272</td>
<td align="center">52,128</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">207</td>
<td align="center">325</td>
</tr>
</tbody>
</table><p>Following shows the map-visualization of grid-based ride-sharing stations in METR-LA and PEMS-BAY.</p>
<p><img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/METR_LA.png" style="zoom:30%;height:800px;width:800px;" /> <img src="https://uctb.github.io/UCTB/sphinx/md_file/src/image/PEMS_BAY.png" style="zoom:30%;height:800px;width:800px;" /></p>
</div>
<div class="section" id="id8">
<h3>6.6.2. Experiment Results<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">15 minutes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">8.93415</td>
<td align="center">3.68983</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">7.02787</td>
<td align="center">2.86893</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">6.44322</td>
<td align="center">2.62339</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">6.37050</td>
<td align="center">2.64524</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">6.64489</td>
<td align="center">2.42605</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">6.44030</td>
<td align="center">5.32297</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">6.38015</td>
<td align="center">2.68953</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">6.15585</td>
<td align="center">2.54368</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">5.64445</td>
<td align="center">2.43292</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">5.79998</td>
<td align="center">2.44947</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">5.78807</td>
<td align="center">2.44571</td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">30 minutes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">9.55981</td>
<td align="center">3.96537</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">9.22951</td>
<td align="center">3.93569</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">8.29796</td>
<td align="center">3.25334</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">8.26941</td>
<td align="center">3.37025</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">8.07924</td>
<td align="center">3.04172</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">8.56215</td>
<td align="center">6.19802</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">7.86569</td>
<td align="center">3.68256</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">7.43553</td>
<td align="center">3.23098</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">7.15628</td>
<td align="center">3.11554</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">6.88889</td>
<td align="center">3.20407</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">7.18431</td>
<td align="center">3.18722</td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center">10.72724</td>
<td align="center">4.01788</td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center">11.73901</td>
<td align="center">5.67008</td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center">10.29861</td>
<td align="center">3.70330</td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center">10.01320</td>
<td align="center">3.70401</td>
</tr>
<tr>
<td align="center">ST_MGCN</td>
<td align="center">10.79813</td>
<td align="center">3.48569</td>
</tr>
<tr>
<td align="center">DCRNN</td>
<td align="center">11.12053</td>
<td align="center">6.91955</td>
</tr>
<tr>
<td align="center">LSTM</td>
<td align="center">10.08317</td>
<td align="center">4.77696</td>
</tr>
<tr>
<td align="center">TMeta-LSTM-GAL</td>
<td align="center">8.66965</td>
<td align="center">3.61642</td>
</tr>
<tr>
<td align="center">STMeta-V1</td>
<td align="center">8.83393</td>
<td align="center">3.51389</td>
</tr>
<tr>
<td align="center">STMeta-V2</td>
<td align="center">9.14697</td>
<td align="center">3.55159</td>
</tr>
<tr>
<td align="center">STMeta-V3</td>
<td align="center">8.99345</td>
<td align="center">3.49954</td>
</tr>
</tbody>
</table></div>
</div>
<div class="section" id="experiment-setting-on-different-datasets">
<h2>6.7. Experiment Setting on different datasets<a class="headerlink" href="#experiment-setting-on-different-datasets" title="Permalink to this headline">¶</a></h2>
<div class="section" id="search-space">
<h3>6.7.1. Search Space<a class="headerlink" href="#search-space" title="Permalink to this headline">¶</a></h3>
<p>We use <a class="reference external" href="https://github.com/microsoft/nni">nni</a> toolkit to search the best parameters of HM, XGBoost and GBRT model. Search space are following.</p>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Model</th>
<th align="center">Search Space</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>CT: 0~6</code>, <code>PT: 0~7</code>, <code>TT: 0~4</code></td>
</tr>
<tr>
<td align="center">ARIMA</td>
<td align="center"><code>CT:168</code>,<code>p:3</code>, <code>d:0</code>, <code>q:0</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>CT: 0~12</code>, <code>PT: 0~14</code>, <code>TT: 0~4</code>, <code>estimater: 10~200</code>, <code>depth: 2~10</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>CT: 0~12</code>, <code>PT: 0~14</code>, <code>TT: 0~4</code>, <code>estimater: 10~200</code>, <code>depth: 2~10</code></td>
</tr>
</tbody>
</table></div>
<div class="section" id="id9">
<h3>6.7.2. Results on Bike<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h3>
<div class="section" id="id10">
<h4>6.7.2.1. Dataset Statistics<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h4>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>New York City</strong></th>
<th align="center"><strong>Chicago</strong></th>
<th align="center"><strong>DC</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2013.03-2017.09</td>
<td align="center">2013.07-2017.09</td>
<td align="center">2013.07-2017.09</td>
</tr>
<tr>
<td align="center">Number of riding records</td>
<td align="center">49,100,694</td>
<td align="center">13,130,969</td>
<td align="center">13,763,675</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">820</td>
<td align="center">585</td>
<td align="center">532</td>
</tr>
</tbody>
</table></div>
<div class="section" id="experiment-setting">
<h4>6.7.2.2. Experiment Setting<a class="headerlink" href="#experiment-setting" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><p>HM &amp; XGBoost &amp; GBRT</p></li>
</ul>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">15 minutes</th>
<th align="center">NYC</th>
<th align="center">Chicago</th>
<th align="center">DC</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>3-1-2</code></td>
<td align="center"><code>5-0-4</code></td>
<td align="center"><code>3-7-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>8-14-4-32-2</code></td>
<td align="center"><code>11-13-4-28-2</code></td>
<td align="center"><code>4-14-4-27-2</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>7-13-4-144-1</code></td>
<td align="center"><code>7-15-4-101-2</code></td>
<td align="center"><code>8-11-5-101-2</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">30 minutes</th>
<th align="center">NYC</th>
<th align="center">Chicago</th>
<th align="center">DC</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>2-1-2</code></td>
<td align="center"><code>3-2-1</code></td>
<td align="center"><code>3-1-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>12-8-1-36-3</code></td>
<td align="center"><code>7-5-2-24-2</code></td>
<td align="center"><code>12-13-4-27-2</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>12-10-0-72-4</code></td>
<td align="center"><code>9-13-2-91-2</code></td>
<td align="center"><code>13-15-5-140-1</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center">NYC</th>
<th align="center">Chicago</th>
<th align="center">DC</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>1-1-3</code></td>
<td align="center"><code>1-1-1</code></td>
<td align="center"><code>2-1-3</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>13-7-0-103-3</code></td>
<td align="center"><code>11-8-0-35-4</code></td>
<td align="center"><code>11-9-5-28-3</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>12-6-1-58-5</code></td>
<td align="center"><code>11-8-1-92-5</code></td>
<td align="center"><code>11-8-5-54-3</code></td>
</tr>
</tbody>
</table><ul>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/ST_MGCN/bike_trial.py">ST_MGCN</a> Run Code &amp; Setting.</p></li>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/DCRNN/bike_trial.py">DCRNN</a> Run Code &amp; Setting.</p></li>
<li><p>LSTM &amp; TMeta-LSTM-GAL &amp; STMeta-V1  &amp; STMeta-V2  &amp; STMeta-V3</p>
<p>These five models can be run by specifying data files and model files on  <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_Obj.py">STMeta_Obj.py</a>.</p>
<p>Data Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/bike_nyc.data.yml">bike_nyc.data.yml</a> , <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/bike_chicago.data.yml">bike_chicago.data.yml</a>, <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/bike_dc.data.yml">bike_dc.data.yml</a></p>
<p>Model Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v0.model.yml">STMeta_v0.model.yml</a>, <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v1.model.yml">STMeta_v1.model.yml</a>.,  <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v2.model.yml">STMeta_v2.model.yml</a>., <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v3.model.yml">STMeta_v3.model.yml</a>.</p>
<ul class="simple">
<li><p>LSTM</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_nyc.data.yml&#39;</span>
          <span class="s1">&#39; -p data_range:0.25,train_data_length:91,graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_nyc.data.yml&#39;</span>
          <span class="s1">&#39; -p data_range:0.5,train_data_length:183,graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_nyc.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_chicago.data.yml&#39;</span>
          <span class="s1">&#39; -p data_range:0.25,train_data_length:91,graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_chicago.data.yml&#39;</span>
          <span class="s1">&#39; -p data_range:0.5,train_data_length:183,graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_chicago.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_dc.data.yml&#39;</span>
          <span class="s1">&#39; -p data_range:0.25,train_data_length:91,graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_dc.data.yml&#39;</span>
          <span class="s1">&#39; -p data_range:0.5,train_data_length:183,graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_dc.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p>TMeta-LSTM-GAL</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_nyc.data.yml -p data_range:0.25,train_data_length:91,graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_nyc.data.yml -p data_range:0.5,train_data_length:183,graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_nyc.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_chicago.data.yml -p data_range:0.25,train_data_length:91,graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_chicago.data.yml -p data_range:0.5,train_data_length:183,graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_chicago.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_dc.data.yml -p data_range:0.25,train_data_length:91,graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_dc.data.yml -p data_range:0.5,train_data_length:183,graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d bike_dc.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p>STMeta-V1</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p>STMeta-V2</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p>STMeta-V3</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_nyc.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_chicago.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.25,train_data_length:91,graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p data_range:0.5,train_data_length:183,graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d bike_dc.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
<p>The result of Bike dataset can be found <a class="reference external" href="./all_results.html#experiment-results">here</a>.</p>
</div>
<div class="section" id="id11">
<h4>6.7.2.3. Experiment Results<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h4>
</div>
</div>
<div class="section" id="id12">
<h3>6.7.3. Results on DiDi<a class="headerlink" href="#id12" title="Permalink to this headline">¶</a></h3>
<div class="section" id="id13">
<h4>6.7.3.1. Dataset Statistics<a class="headerlink" href="#id13" title="Permalink to this headline">¶</a></h4>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>Xi'an</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2016.10-2016.11</td>
<td align="center">2016.10-2016.11</td>
</tr>
<tr>
<td align="center">Number of records</td>
<td align="center">5,922,961</td>
<td align="center">8,439,537</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">256</td>
<td align="center">256</td>
</tr>
</tbody>
</table></div>
<div class="section" id="id14">
<h4>6.7.3.2. Experiment Setting<a class="headerlink" href="#id14" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><p>HM &amp; XGBoost &amp; GBRT</p></li>
</ul>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">15 minutes</th>
<th align="center"><strong>Xi'an</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>5-0-4</code></td>
<td align="center"><code>2-7-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>7-14-0-10-4</code></td>
<td align="center"><code>12-14-1-27-3</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>11-2-2-45-3</code></td>
<td align="center"><code>13-15-5-39-3</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">30 minutes</th>
<th align="center"><strong>Xi'an</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>2-0-2</code></td>
<td align="center"><code>1-7-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>9-0-2-25-3</code></td>
<td align="center"><code>9-14-3-16-3</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>9-0-2-80-3</code></td>
<td align="center"><code>10-10-5-34-3</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center"><strong>Xi'an</strong></th>
<th align="center"><strong>Chengdu</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>1-1-2</code></td>
<td align="center"><code>0-7-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>12-0-2-10-5</code></td>
<td align="center"><code>9-6-2-14-3</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>9-0-2-50-2</code></td>
<td align="center"><code>9-12-2-50-5</code></td>
</tr>
</tbody>
</table><ul class="simple">
<li><p>ST-ResNet</p></li>
</ul>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">ST-ResNet Search Space</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center"><code>residual_units:2~6</code>,  <code>conv_filter:[32, 64, 128]</code>,  <code>kernal_size:3~5</code>, <br /><code>lr:[0.0001, 0.00002, 0.00004, 0.00008, 0.00001]</code>, <code>batch_size:[32, 64, 128, 256]</code></td>
</tr>
</tbody>
</table><p>The best parameters found are following.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">args</span> <span class="o">=</span> <span class="p">{</span>
  <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="s1">&#39;DiDi&#39;</span><span class="p">,</span>
  <span class="s1">&#39;city&#39;</span><span class="p">:</span> <span class="s1">&#39;Chengdu&#39;</span><span class="p">,</span>
  <span class="s1">&#39;num_residual_unit&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
  <span class="s1">&#39;conv_filters&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
  <span class="s1">&#39;kernel_size&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
  <span class="s1">&#39;lr&#39;</span><span class="p">:</span> <span class="mf">1e-5</span><span class="p">,</span>
  <span class="s1">&#39;batch_size&#39;</span><span class="p">:</span> <span class="mi">32</span>
<span class="p">}</span>
</pre></div>
</div>
<p>We can modify <code class="docutils literal notranslate"><span class="pre">city</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">Chengdu</span></code> or <code class="docutils literal notranslate"><span class="pre">Xian</span></code> in <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/ST_ResNet/ST_ResNet.py">ST_ResNet.py</a> , and then run it.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span> python ST_ResNet.py 
</pre></div>
</div>
<ul>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/ST_MGCN/didi_trial.py">ST_MGCN</a> Run Code &amp; Setting.</p></li>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/DCRNN/didi_trial.py">DCRNN</a> Run Code &amp; Setting.</p></li>
<li><p>LSTM &amp; TMeta-LSTM-GAL &amp; STMeta-V1  &amp; STMeta-V2  &amp; STMeta-V3</p>
<p>These five models can be run by specifying data files and model files on <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_Obj.py">STMeta_Obj.py</a>.</p>
<p>Data Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/didi_xian.data.yml">didi_xian.data.yml</a> , <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/didi_chengdu.data.yml">didi_chengdu.data.yml</a>.</p>
<p>Model Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v0.model.yml">STMeta_v0.model.yml</a>, <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v1.model.yml">STMeta_v1.model.yml</a>.,  <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v2.model.yml">STMeta_v2.model.yml</a>., <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v3.model.yml">STMeta_v3.model.yml</a>.</p>
<ul>
<li><p>LSTM</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_xian.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_xian.data.yml&#39;</span>
        <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_xian.data.yml&#39;</span>
        <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_chengdu.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_chengdu.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_chengdu.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>TMeta-LSTM-GAL</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_xian.data.yml -p graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_xian.data.yml -p graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_xian.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_chengdu.data.yml -p graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_chengdu.data.yml -p graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d didi_chengdu.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V1</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V2</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V3</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d didi_xian.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d didi_chengdu.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Interaction,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
</li>
</ul>
<p>The result of DiDi dataset can be found <a class="reference external" href="./all_results.html#id2">here</a>.</p>
</div>
</div>
<div class="section" id="id15">
<h3>6.7.4. Results on Metro<a class="headerlink" href="#id15" title="Permalink to this headline">¶</a></h3>
<div class="section" id="id16">
<h4>6.7.4.1. Dataset Statistics<a class="headerlink" href="#id16" title="Permalink to this headline">¶</a></h4>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>Chongqing</strong></th>
<th align="center"><strong>Shanghai</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2016.08-2017.07</td>
<td align="center">2016.07-2016.09</td>
</tr>
<tr>
<td align="center">Number of records</td>
<td align="center">409,277,117</td>
<td align="center">333,149,034</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">113</td>
<td align="center">288</td>
</tr>
</tbody>
</table></div>
<div class="section" id="id17">
<h4>6.7.4.2. Experiment Setting<a class="headerlink" href="#id17" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><p>HM &amp; XGBoost &amp; GBRT</p></li>
</ul>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">15 minutes</th>
<th align="center"><strong>Chongqing</strong></th>
<th align="center"><strong>Shanghai</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>2-1-4</code></td>
<td align="center"><code>1-0-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>12-6-4-51-8</code></td>
<td align="center"><code>11-10-4-31-7</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>12-14-1-182-7</code></td>
<td align="center"><code>12-7-1-148-5</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">30 minutes</th>
<th align="center"><strong>Chongqing</strong></th>
<th align="center"><strong>Shanghai</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>1-0-4</code></td>
<td align="center"><code>1-1-3</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>11-5-0-45-8</code></td>
<td align="center"><code>12-6-1-206-3</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>10-3-0-107-8</code></td>
<td align="center"><code>7-4-1-58-7</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center"><strong>Chongqing</strong></th>
<th align="center"><strong>Shanghai</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>0-1-4</code></td>
<td align="center"><code>0-0-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>9-14-2-200-5</code></td>
<td align="center"><code>3-7-0-50-5</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>12-10-4-200-5</code></td>
<td align="center"><code>9-5-1-66-6</code></td>
</tr>
</tbody>
</table><ul>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/ST_MGCN/metro_trial.py">ST_MGCN</a> Run Code &amp; Setting.</p></li>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/DCRNN/metro_trial.py">DCRNN</a> Run Code &amp; Setting.</p></li>
<li><p>LSTM &amp; TMeta-LSTM-GAL &amp; STMeta-V1  &amp; STMeta-V2  &amp; STMeta-V3</p>
<p>These five models can be run by specifying data files and model files on <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_Obj.py">STMeta_Obj.py</a>.</p>
<p>Data Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/metro_chongqing.data.yml">metro_chongqing.data.yml</a> , <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/metro_shanghai.data.yml">metro_shanghai.data.yml</a>.</p>
<p>Model Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v0.model.yml">STMeta_v0.model.yml</a>, <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v1.model.yml">STMeta_v1.model.yml</a>.,  <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v2.model.yml">STMeta_v2.model.yml</a>., <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v3.model.yml">STMeta_v3.model.yml</a>.</p>
<ul>
<li><p>LSTM</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_chongqing.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_chongqing.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_chongqing.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_shanghai.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_shanghai.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_shanghai.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>TMeta-LSTM-GAL</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_chongqing.data.yml -p graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_chongqing.data.yml -p graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_chongqing.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_shanghai.data.yml -p graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_shanghai.data.yml -p graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metro_shanghai.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V1</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V2</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V3</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d metro_chongqing.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d metro_chongqing.data.yml &#39;</span>
        <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml -d metro_shanghai.data.yml &#39;</span>
          <span class="s1">&#39;-p graph:Distance-Correlation-Line,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
</li>
</ul>
<p>The result of Metro dataset can be found <a class="reference external" href="./all_results.html#id4">here</a>.</p>
</div>
</div>
<div class="section" id="id18">
<h3>6.7.5. Results on Charge-Station<a class="headerlink" href="#id18" title="Permalink to this headline">¶</a></h3>
<div class="section" id="id19">
<h4>6.7.5.1. Dataset Statistics<a class="headerlink" href="#id19" title="Permalink to this headline">¶</a></h4>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>Beijing</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2018.03-2018.05</td>
</tr>
<tr>
<td align="center">Number of records</td>
<td align="center">1,272,961</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">629</td>
</tr>
</tbody>
</table></div>
<div class="section" id="id20">
<h4>6.7.5.2. Experiment Setting<a class="headerlink" href="#id20" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><p>HM &amp; XGBoost &amp; GBRT</p></li>
</ul>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Beijing</th>
<th align="center">30 minutes</th>
<th align="center">60 minutes</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>2-0-0</code></td>
<td align="center"><code>2-0-2</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>6-6-1-19-2</code></td>
<td align="center"><code>12-7-0-20-2</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>13-3-2-47-3</code></td>
<td align="center"><code>12-10-0-100-2</code></td>
</tr>
</tbody>
</table><ul>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/ST_MGCN/cs_trial.py">ST_MGCN</a> Run Code &amp; Setting.</p></li>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/DCRNN/cs_trial.py">DCRNN</a> Run Code &amp; Setting.</p></li>
<li><p>LSTM &amp; TMeta-LSTM-GAL &amp; STMeta-V1  &amp; STMeta-V2  &amp; STMeta-V3</p>
<p>These five models can be run by specifying data files and model files on <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_Obj.py">STMeta_Obj.py</a>.</p>
<p>Data Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/chargestation_beijing.data.yml">chargestation_beijing.data.yml</a>.</p>
<p>Model Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v0.model.yml">STMeta_v0.model.yml</a>, <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v1.model.yml">STMeta_v1.model.yml</a>.,  <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v2.model.yml">STMeta_v2.model.yml</a>., <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v3.model.yml">STMeta_v3.model.yml</a>.</p>
<ul>
<li><p>LSTM</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d chargestation_beijing.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:1&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d chargestation_beijing.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:2&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>TMeta-LSTM-GAL</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance,MergeIndex:1&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance,MergeIndex:2&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V1</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
          <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance-Correlation,MergeIndex:1&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
        <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance-Correlation,MergeIndex:2&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V2</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
          <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance-Correlation,MergeIndex:1&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
        <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance-Correlation,MergeIndex:2&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V3</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance-Correlation,MergeIndex:1&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d chargestation_beijing.data.yml -p graph:Distance-Correlation,MergeIndex:2&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
</li>
</ul>
<p>The result of Charge-Station dataset can be found <a class="reference external" href="./all_results.html#id6">here</a>.</p>
</div>
</div>
<div class="section" id="id21">
<h3>6.7.6. Results on Traffic Speed<a class="headerlink" href="#id21" title="Permalink to this headline">¶</a></h3>
<div class="section" id="id22">
<h4>6.7.6.1. Dataset Statistics<a class="headerlink" href="#id22" title="Permalink to this headline">¶</a></h4>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">Attributes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Time span</td>
<td align="center">2012.03-2012.06</td>
<td align="center">2017.01-2017.07</td>
</tr>
<tr>
<td align="center">Number of riding records</td>
<td align="center">34,272</td>
<td align="center">52,128</td>
</tr>
<tr>
<td align="center">Number of stations</td>
<td align="center">207</td>
<td align="center">325</td>
</tr>
</tbody>
</table></div>
<div class="section" id="id23">
<h4>6.7.6.2. Experiment Setting<a class="headerlink" href="#id23" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><p>HM &amp; XGBoost &amp; GBRT</p></li>
</ul>
<table border="1" class="docutils">
<thead>
<tr>
<th align="center">15 minutes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>2-0-4</code></td>
<td align="center"><code>1-0-1</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>11-1-2-25-3</code></td>
<td align="center"><code>12-4-2-21-4</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>11-8-2-29-4</code></td>
<td align="center"><code>10-6-1-65-6</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">30 minutes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>2-0-4</code></td>
<td align="center"><code>1-0-1</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>6-6-0-25-3</code></td>
<td align="center"><code>12-13-2-27-3</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>10-0-0-27-3</code></td>
<td align="center"><code>12-6-2-90-7</code></td>
</tr>
</tbody>
</table><table border="1" class="docutils">
<thead>
<tr>
<th align="center">60 minutes</th>
<th align="center"><strong>METR-LA</strong></th>
<th align="center"><strong>PEMS-BAY</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">HM</td>
<td align="center"><code>2-1-4</code></td>
<td align="center"><code>1-1-4</code></td>
</tr>
<tr>
<td align="center">XGBoost</td>
<td align="center"><code>2-2-0-25-3</code></td>
<td align="center"><code>12-6-2-19-3</code></td>
</tr>
<tr>
<td align="center">GBRT</td>
<td align="center"><code>4-5-1-19-4</code></td>
<td align="center"><code>12-7-2-59-5</code></td>
</tr>
</tbody>
</table><ul>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/ST_MGCN/metr_trial.py">METR-LA</a>  and <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/ST_MGCN/pems_trial.py">PEMS-BAY</a>  ST_MGCN Run Code &amp; Setting.</p></li>
<li><p><a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/DCRNN/metr_trial.py">METR-LA</a> and <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/DCRNN/pems_trial.py">PEMS-BAY</a> DCRNN Run Code &amp; Setting.</p></li>
<li><p>LSTM &amp; TMeta-LSTM-GAL &amp; STMeta-V1  &amp; STMeta-V2  &amp; STMeta-V3</p>
<p>These five models can be run by specifying data files and model files on <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_Obj.py">STMeta_Obj.py</a>.</p>
<p>Data Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/metr_la.data.yml">metr_la.data.yml</a> , <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/pems_bay.data.yml">pems_bay.data.yml</a>.</p>
<p>Model Files: <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v0.model.yml">STMeta_v0.model.yml</a>, <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v1.model.yml">STMeta_v1.model.yml</a>.,  <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v2.model.yml">STMeta_v2.model.yml</a>., <a class="reference external" href="https://github.com/Di-Chai/UCTB/blob/master/Experiments/STMeta/STMeta_v3.model.yml">STMeta_v3.model.yml</a>.</p>
<ul>
<li><p>LSTM</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metr_la.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metr_la.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d metr_la.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d pems_bay.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d pems_bay.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml -d pems_bay.data.yml&#39;</span>
          <span class="s1">&#39; -p graph:Distance,period_len:0,trend_len:0,mark:LSTMC,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>TMeta-LSTM-GAL</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v0.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V1</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v1.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V2</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v2.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>STMeta-V3</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d metr_la.data.yml -p graph:Distance-Correlation,MergeIndex:12&#39;</span><span class="p">)</span>

<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:3&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:6&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;python STMeta_Obj.py -m STMeta_v3.model.yml&#39;</span>
          <span class="s1">&#39; -d pems_bay.data.yml -p graph:Distance-Correlation,MergeIndex:12&#39;</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
</li>
</ul>
<p>The results of METR-LA and PEMS-BAY can be found <a class="reference external" href="./all_results.html#id7">here</a>.</p>
</div>
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